From helping doctors make faster and more reliable diagnoses, to powering new frontiers in space exploration, AI has made a huge impact across industries, and we witness it every day.
Continue reading These are The News: A curated list of AI & machine learning newsletters
Currently, different domains of machine learning software and hardware have different compiler infrastructures. There are number of challenges posed by this dynamic, including:
MLIR seeks to address this software fragmentation by building a reusable and extensible compiler infrastructure. In this piece, we’ll look at a conceptual view of MLIR.
MLIR seeks to promote the design and implementation of code generators, optimizers, and translators at various stages of abstraction across different application domains. The need for MLIR arose from the realization that modern machine learning frameworks have different runtimes, compilers, and graph technologies. For example, TensorFlow itself has different compilers for different frameworks.
It would be great if we as devs got to play with limitless memory and never had to care about working with it judiciously. Unfortunately, that isn’t true, and hence, we have to behave like a renter to the OS—rent the memory for a while, use the memory, and then hand it back.
Swift is a smart language, and it knows that many devs don’t like handing the memory back to the environment; hence, it keeps track of the allocated memory using a mechanism called ARC (automatic reference counting).
Developing with the camera on Android can be difficult. When we need to develop a camera-based application, we need to do a lot of manual work, and we need to do handle a lot of complex things with the Camera API, like handling preview screens, image rotations, and much more
At Google IO 2019, Google added another powerful tool for camera development in Android called CameraX as part of Jetpack.
In this post, we are going to investigate a relatively newer field in deep learning which involves graphs — a very important and widely used data structure. This post encompasses the basics of graphs, the amalgamation of graphs and deep learning, and a basic idea about graph neural networks and their applications. We will also briefly discuss on how to build graphs with a Python library called NetworkX
Continue reading Learn how to display and manage notifications in Android
Processing a couple of gigabytes of data on one’s laptop is usually an uphill task, unless the laptop has high RAM and a whole lot of compute power.
That notwithstanding, data scientists still have to look for alternative solutions to deal with this problem. Some of the hacks involve tweaking Pandas to enable it to process huge datasets, buying a GPU machine, or purchasing compute power on the cloud. In this piece, we’ll see how we can use Dask to work with large datasets on our local machines.
You’ve got a great product, and you want to match this up with the way your mobile application looks and feels. This kind of consistency matters for creating engaging user experiences. And let’s say, in this app, you need to use maps in a way that fits your brand.
Luckily, there’s a way to easily create custom styles on Google Maps. With custom styling, you can change the look and feel of your map according to your desired needs.
Image colorization may have been reserved for those with artistic talent in the past, but now anyone with basic programming knowledge can get in on the action. Thanks to projects like DeOldify, we can colorize black and white images and video with minimal setup! The project even includes pre-trained weights, so you don’t need to spend time training on your machine.
Continue reading DeOldify: Visualizing Image Colorization with Hyper-Realistic Artwork
Traditionally, AI solutions have needed a large amount of parallel computational processing power. So for a long time, a requirement of AI-based service was server-based Internet connectivity. But solutions that require real-time action need on-device computation—this is where edge AI enters the picture.
You can use GPU-based devices, but it makes the process costly, and with this come the problems of bloated size and high energy consumption. But more and more, edge AI is becoming an essential part of the ongoing deep learning revolution, both in terms of research and innovation.
Continue reading Edge TPU: Hands-On with Google’s Coral USB Accelerator
Successful machine learning models are developed to serve and bring value to an end user. Whether the end user is a customer or domain expert, the full value of data science is only realized by operationalizing the workflow and exposing model predictions and insights to the end user. End users consume and interact with models in different contexts—via a web application, mobile application, or a command line API request.
Continue reading Deploying a Text Classification Model Using Flask and Vue.js
